Echo cancellation is a requirement in many applications such as speakerphones, entertainment theater systems, and audio digital signal processing. However, current echo cancellation methods either cancel only linear echo, or are very computation-intensive in order to provide acceptable nonlinear echo modeling accuracy. For example, most echo cancellation systems in the frequency domain comprise two components: linear adaptive echo cancellation (LAEC) and nonlinear suppression of residual echo (NLP). Current methods of nonlinear echo suppression attempt to remove residual echo after LAEC application, utilizing echo return loss (ERL) and/or echo return loss enhancement (ERLE). Where ERL or ERL and ERLE are above a certain threshold NLP attempts to remove the residual echo and insert comfort noise. Such methods may be useful where the linear echo canceller adequately removes linear echo and nonlinear echo is small. However, such methods may unacceptably clip or distort soft voices spoken by local speakers.
In addition, current nonlinear echo cancellation techniques based on Volterra filters, for example, require many taps to achieve accurate modeling of the nonlinear echo. Consequently, accepted adaptive filtering algorithms for echo cancellation have high computation costs and tend to converge very slowly. Moreover, current nonlinear echo cancellation techniques are based on the correlation between echo and residual echo or between far-end and near-end signals in the same frequency band. As a result, current echo suppression techniques essentially suppress the linear echo component in a particular frequency band. Accordingly, such techniques may be used to suppress nonlinear echo via overestimation of the linear echo only if the nonlinear echo is very small. However, in general, these current methods cannot accurately suppress the nonlinear echo.